CICI:TCR:SAFARI: Scientific Analytics, Forensics, and Reproducibility for Workflows in CI

About This Grant

The SAFARI project (Scientific Analytics, Forensics, and Reproducibility for Workflows in CI) advances scientific research by integrating forensic data analytics into the workflows used for large-scale data analyses. These analytics (i.e., tools that track the origin, processing, and reliability of data) are directly incorporated into the workflow systems used by researchers in Earth science and related fields. The integration ensures that workflow artifacts (data and software) are reliable, reusable, and reproducible and that the scientific results are trustworthy and of high quality. SAFARI enables researchers to construct and execute workflows that are modular, transparent, and explainable, making it easier to adapt and share methods across diverse computing platforms with collaborators and the public. The benefits of this project are demonstrated in applications such as weather forecasting, irrigation modeling, and wildfire prevention, with broader potential impacts in bioinformatics, physics, and materials science. SAFARI enhances the reliability, security, and resilience of data from high-throughput scientific workflows by embedding forensic data analytics into cyberinfrastructure services. Rather than applying post-hoc analysis, it integrates provenance tracking, automated verification, and artifact modularization into the Pegasus Workflow Management System. SAFARI targets three core challenges: ensuring trustworthiness through transparent and secure execution, improving reusability via workflow decomposition and containerization, and supporting reproducibility with standardized provenance capture and validation. The project addresses threats such as incomplete documentation, tampering risks, and execution variability—issues commonly observed in workflows that utilize Artificial Intelligence (AI) and heterogeneous geospatial data to predict environmental conditions and natural hazards such as soil moisture, wildfire risk, and crop yield estimation. By restructuring workflows into reusable components and forming an Earth science-focused group aligned with the high performance computing community, SAFARI delivers scalable and secure Cyberinfrastructure (CI) services. Through impactful Earth science applications, such as AI-powered soil moisture modeling and wildfire prediction, SAFARI demonstrates practical societal benefits directly aligned with national AI priorities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.